2024/09/10 by Xiaoxiao Cheng, Cheng, Xiaoxiao, Shen, Shixian +8 · 1 citation
Computer Science · Neuroscience · #FOS: Computer and information sciences #Gaze Tracking and Assistive Technology #Human-Computer Interaction (cs.HC) #Tactile and Sensory Interactions #Visual perception and processing mechanisms
paper · pdf · doi:10.48550/arxiv.2409.06124
openalex publication_date 2024/09/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Humans activate muscles to shape the mechanical interaction with their environment, but can they harness this control mechanism to best sense the environment? We investigated how participants adapt their muscle activation to visual and haptic information when tracking a randomly moving target with a robotic interface. The results exhibit a differentiated effect of these sensory modalities, where participants' muscle cocontraction increases with the haptic noise and decreases with the visual noise, in apparent contradiction to previous results. These results can be explained, and reconciled with previous findings, when considering muscle spring like mechanics, where stiffness increases with cocontraction to regulate motion guidance. Increasing cocontraction to more closely follow the motion plan favors accurate visual over haptic information, while decreasing it avoids injecting visual noise and relies on accurate haptic information. We formulated this active sensing mechanism as the optimization of visuo-haptic information and effort. This OIE model can explain the adaptation of muscle activity to unimodal and multimodal sensory information when interacting with fixed or dynamic environments, or with another human, and can be used to optimize human-robot interaction.